The demo app is really four apps, but we’re going to focus on the “TF Classify” only.

TF Classify opens your camera, and classifies whatever objects you show it. The really mind blowing thing is that this works totally offline — you do not need an internet connection. I had a lot of fun with this.

It prints out the object classification along with a confidence level (1.000 for perfect confidence, 0.000 for zero confidence). When your object fills most of the image, it often does pretty well.

NOTE: Bazel does not currently support building for Android on Windows. Full support for gradle/cmake builds is coming soon, but in the meantime we suggest that Windows users download the prebuilt binaries instead.

Install Bazel and Android Prerequisites

Bazel is the primary build system for TensorFlow. To build with Bazel, it and the Android NDK and SDK must be installed on your system.

The Android NDK is required to build the native (C/C++) TensorFlow code. The current recommended version is 12b, which may be found here.

The Android SDK and build tools may be obtained here, or alternatively as part of Android Studio. Build tools API >= 23 is required to build the TF Android demo (though it will run on API >= 21 devices).

Edit WORKSPACE

The Android entries in <workspace_root>/WORKSPACE must be uncommented with the paths filled in appropriately depending on where you installed the NDK and SDK. Otherwise an error such as: "The external label '//external:android/sdk' is not bound to anything" will be reported.

Also edit the API levels for the SDK in WORKSPACE to the highest level you have installed in your SDK. This must be >= 23 (this is completely independent of the API level of the demo, which is defined in AndroidManifest.xml). The NDK API level may remain at 14.

Build

After editing your WORKSPACE file to update the SDK/NDK configuration, you may build the APK. Run this from your workspace root:

bazel build -c opt //tensorflow/examples/android:tensorflow_demo

Install

Make sure that adb debugging is enabled on your Android 5.0 (API 21) or later device, then after building use the following command from your workspace root to install the APK:

วันจันทร์ที่ 13 มีนาคม พ.ศ. 2560

Machine Learning TensorFlow Android App Demo

What is TensorFlow?TensorFlow is open source machine learning library from Google. Computation code is written in C++, but programmers can write their TensorFlow software in either C++ or Python and implemented for CPUs ,GPUs or both.

In November 2015, Google announced and open sourced TensorFlow, its latest and greatest machine learning library. This is a big deal for three reasons:

Machine Learning expertise: Google is a dominant force in machine learning. Its prominence in search owes a lot to the strides it achieved in machine learning.

Scalable : the announcement noted that TensorFlow was initially designed for internal use and that it’s already in production for some live product features.

Ability to run on Mobile.

This last reason is the operating reason for this post since we’ll be focusing on Android. If you examine the tensorflow repo on GitHub, you’ll find a little tensorflow/examples/android directory. I’ll try to shed some light on the Android TensorFlow example and some of the things going on under the hood.

Install TensorFlow on your System ( PC or Notebook , Windows or mac )

It has many method to install

virtualenv

pip

Docker

installing from sources

We use pip method first.

Installing with native pip

Python

In order to install TensorFlow, your system must contain one of the following Python versions:

Python 2.7

Python 3.3+

Pip installs and manages software packages written in Python. If you intend to install with native pip, then one of the following flavors of pip must be installed on your system:

pip, for Python 2.7

pip3, for Python 3.n.

pip or pip3was probably installed on your system when you installed Python. To determine whether pip or pip3 is actually installed on your system, issue one of the following commands:

$ pip -V # for Python 2.7

$ pip3 -V # for Python 3.n

We strongly recommend pip or pip3 version 8.1 or higher in order to install TensorFlow. If pip or pip3 8.1 or later is not installed, issue the following commands to install or upgrade:

SC-widgets

This is a library of widgets.The 2.x version change completely the way to draw using the ScDrawer as base for create the ScGauge and all classes inherited from it. This using a path to follow and applying some features to draw extra on the path. This way to think leaves a lot of freedom to the users to create particular components limited only by his imagination.

วันพฤหัสบดีที่ 15 ธันวาคม พ.ศ. 2559

Android Things with Raspberry Pi Board

What is Android Things?

On 13 December 2016,
Google launched a version of Android called Android Things.
Android Things (formerly known as Brillo)

Android Things are IoT Platform for Android Developer.Use Development tools and ecosystem the same Android App Development. So you can use Android Studio to develop Android Things.
and without previous knowledge of embedded system design.

Understand the Android Things Platform
Android Things has a few key differences compared to the core Android OS, read the Overview to understand key concepts that you'll need to understand.

Hardware Supported

Intel Edison
NXP Pico
Raspberry Pi 3

Software

Google has released the SDK preview of Android Things.
You can learn more about that (and key Android Things concepts)
Unfortunately,
we’re not entirely sure when the full version will go live just yet.

วันอาทิตย์ที่ 13 พฤศจิกายน พ.ศ. 2559

RGBLED IoT ESP8266 with NetPie

This Project you can control RGBLED Lights on the internet we call Internet of Things ( IoT ).

What is NETPIE ?

NETPIE platform is a cloud-based platform-as-a-service that facilitates interconnecting IoT devices (“things”) together in a most seamless and transparent manner possible by pushing the complexity of connecting IoT devices from the hands of application developers or device manufacturers to the cloud.https://netpie.io/

System Diagram

Hardware

1.ESP8266 WiFi Module ( or NodeMCU )

2.RGB LED ( common anode (A) ) if you to use common cathode ( K ) must edit some code.